Multi-Information Preprocessing Event Extraction With BiLSTM-CRF Attention for Academic Knowledge Graph Construction
Datos Bibliográficos
| ID | 22108563 |
|---|---|
| Autores | Chao Chang (0000-0002-5844-8014, South China Normal University), Yong Tang (0000-0002-2094-8582, South China Normal University), Yongxu Long (South China Normal University), Kun Hu (0000-0003-0350-3132, South China Normal University), Ying Li (0000-0003-0678-9535, South China Normal University), Jianguo Li (0000-0002-1147-4864, South China Normal University), Chang-Dong Wang (0000-0001-5972-559X, Sun Yat-sen University) |
| Año | 2023 |
| Volumen | 10 |
| Número | 5 |
| Páginas | 2713-2724 |
| Fecha de publicación | 2023-10-01 |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Identificadores de la revista | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Editorial | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2022.3183685 |
| OpenAlex | W4285195691 |
| Idioma | EN |
| Citas recibidas | 4 |
| Referencias citadas | 35 |
Academic knowledge graph is an important application of knowledge graph in the vertical field of academia. At present, the construction of the academic knowledge graph is mainly completed by extracting published academic papers, authors, publications, and other information from related databases. However, academic information is not just information of published papers. Scholars’ academic activities include participation in academic conferences, visiting and making presentation, and so on. However, the above academic information is hidden in natural language texts and cannot be directly stored in academic knowledge graph. This article proposes an approach named construct-SCHOLAT knowledge graph to construct an academic event knowledge graph based on academic social network SCHOLAT. The construction framework mainly consists of two parts: data preprocessing and event extraction. In the data preprocessing, we propose a knowledge graph embedding method to represent scholars’ academic social feature. In the event extraction, we concatenate the preprocessed scholar vector with academic we-media blog text into the extraction model based on BiLSTM-CRF fused with attention mechanism. The extracted events are added to academic knowledge graph, and a public relationship exists between the event and the scholar. Compared to the previous methods, our framework has an excellent performance after experimental verification. To the best of our knowledge, this is the first study to use the scholar academic social information of the scholar who edited the text as the event extraction input information. In addition, we publish a Chinese event extraction dataset SCHOLAT academic event extraction. 1 The dataset includes academic we-media blog and the social behavior embedding vector of the scholar. All the data in this dataset are derived from the academic social network SCHOLAT. 1 https://www.scholat.com/research/opendata
Data science · Graph · Information extraction · Information retrieval · Knowledge extraction · Preprocessor · Social media · World Wide Web · Advanced Graph Neural Networks · Computer Science · Topic Modeling · Web Data Mining and Analysis · Artificial Intelligence · Theoretical Computer Science
| Citas por año | 2 |
|---|---|
| Intervalo de citas | 2024 - 2025 (2) |
| Altamente citado | No |